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Research Paper | Computer Science & Engineering | India | Volume 12 Issue 8, August 2023 | Popularity: 5.6 / 10
Enhancing IoT Cybersecurity with Graph Neural Networks: Advanced Anomaly Detection and Threat Mitigation
Harish Narne
Abstract: The increasing adoption of Internet of Things (IoT) devices has transformed industries by enabling seamless connectivity and data - driven operations. However, the interconnected nature of IoT networks has introduced significant cybersecurity risks, including malware attacks, Distributed Denial of Service (DDoS) attacks, and data breaches, which can compromise entire ecosystems. Traditional cybersecurity measures, such as firewalls and intrusion detection systems, struggle to address IoT environments' dynamic, heterogeneous, and resource - constrained nature. Graph Neural Networks (GNNs) have emerged as a powerful tool to tackle these challenges by leveraging the graph - based structure of IoT networks. This paper presents a novel methodology to enhance IoT cybersecurity through GNNs by capturing complex device interactions, detecting anomalies, and mitigating cyber threats in real time. Key contributions include advanced graph representation learning techniques, scalable GNN architectures, and their integration with existing security systems. Experimental evaluations using benchmark datasets and simulated IoT environments demonstrate superior accuracy (95%) in anomaly detection, reduced false - positive rates (by 30%), and real - time threat mitigation capabilities. The proposed approach also addresses scalability across large IoT networks and cross - domain generalization, making it suitable for diverse applications, including industrial IoT (IIoT), smart homes, and critical infrastructure. This study highlights the transformative potential of GNNs in advancing IoT cybersecurity and provides a roadmap for developing resilient, adaptive, and cost - effective solutions in the face of an evolving threat landscape.
Keywords: Graph Neural Networks, IoT Cybersecurity, Anomaly Detection, Threat Mitigation, Graph Embeddings, Network Resilience, Real - Time Protection, Model Optimization, Scalable IoT Security
Edition: Volume 12 Issue 8, August 2023
Pages: 2571 - 2575
DOI: https://www.doi.org/10.21275/SR23086105929
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